DeepSTL -- From English Requirements to Signal Temporal Logic
This addresses the difficulty of writing formal specifications for cyber-physical systems, making formal methods more accessible, though it is incremental as it builds on existing translation techniques.
The authors tackled the challenge of translating informal English requirements into Signal Temporal Logic (STL) by developing DeepSTL, a tool that uses a two-step workflow involving synthetic data generation and transformer-based neural translation, achieving high translation quality for well-trained patterns.
Formal methods provide very powerful tools and techniques for the design and analysis of complex systems. Their practical application remains however limited, due to the widely accepted belief that formal methods require extensive expertise and a steep learning curve. Writing correct formal specifications in form of logical formulas is still considered to be a difficult and error prone task. In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry. A major challenge to devise such a translator is the lack of publicly available informal requirements and formal specifications. We propose a two-step workflow to address this challenge. We first design a grammar-based generation technique of synthetic data, where each output is a random STL formula and its associated set of possible English translations. In the second step, we use a state-of-the-art transformer-based neural translation technique, to train an accurate attentional translator of English to STL. The experimental results show high translation quality for patterns of English requirements that have been well trained, making this workflow promising to be extended for processing more complex translation tasks.